Random forest algorithm course, in this course participants will delve into the intricacies of Random Forests, understanding how they harness the collective wisdom of multiple decision trees to deliver robust predictions. Starting with the fundamentals, learners will uncover the mechanics of building individual decision trees within the Random Forest ensemble, exploring concepts like feature sampling and bootstrapping. Through practical exercises and real-world examples, students will grasp how Random Forests mitigate overfitting and handle noisy datasets, making them versatile tools for various domains. Furthermore, the course delves into advanced topics such as hyperparameter tuning and feature importance analysis, empowering participants to optimize and interpret Random Forest models effectively. By the end of the course, students will possess the knowledge and skills to leverage Random Forest Algorithm confidently for predictive modeling tasks, making informed decisions and extracting valuable insights from data.